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Volumn , Issue , 2012, Pages 209-214

Integration of structural expert knowledge about classes for classification using the fuzzy supervised neural gas

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING SYSTEMS; MEDICAL APPLICATIONS; NEURAL NETWORKS;

EID: 84887048792     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (5)

References (13)
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    • [Turkish]
    • P. Günther, T. Villmann, and W. Hermann. Event related potentials and cognitive evaluation in Wilson's disease with and without neurological manifestation. Journal of Neurological Sciences [Turkish], 28(1):79-85, 2011.
    • (2011) Journal of Neurological Sciences , vol.28 , Issue.1 , pp. 79-85
    • Günther, P.1    Villmann, T.2    Hermann, W.3
  • 3
    • 0036791938 scopus 로고    scopus 로고
    • Generalized relevance learning vector quantization
    • B. Hammer and T. Villmann. Generalized relevance learning vector quantization. Neural Networks, 15(8-9):1059-1068, 2002.
    • (2002) Neural Networks , vol.15 , Issue.8-9 , pp. 1059-1068
    • Hammer, B.1    Villmann, T.2
  • 4
    • 21644451517 scopus 로고    scopus 로고
    • Klassifikation des Morbus Wilson auf der Basis neurophysiologischer Parameter
    • W. Hermann, P. Günther, A. Wagner, and T. Villmann. Klassifikation des Morbus Wilson auf der Basis neurophysiologischer Parameter. Der Nervenarzt, 76:733-739, 2005.
    • (2005) Der Nervenarzt , vol.76 , pp. 733-739
    • Hermann, W.1    Günther, P.2    Wagner, A.3    Villmann, T.4
  • 6
    • 0242386626 scopus 로고    scopus 로고
    • Elektrophysiologisches Schädigungsprofil von Patienten mit einem Morbus Wilson'
    • W. Hermann, T. Villmann, and A. Wagner. Elektrophysiologisches Schädigungsprofil von Patienten mit einem Morbus Wilson'. Der Nervenarzt, 74(10):881-887, 2003.
    • (2003) Der Nervenarzt , vol.74 , Issue.10 , pp. 881-887
    • Hermann, W.1    Villmann, T.2    Wagner, A.3
  • 7
    • 84947743158 scopus 로고    scopus 로고
    • Fuzzy supervised neural gas for semi-supervised vector quantization - Theoretical aspects
    • MLR-02-2011, ISSN:1865-3960
    • M. Kästner and T. Villmann. Fuzzy supervised neural gas for semi-supervised vector quantization - theoretical aspects. Machine Learning Reports, 5(MLR-02-2011):1-16, 2011. ISSN:1865-3960, http://www.techfak.uni-bielefeld.de/~fschleif/mlr/mlr_02_2011.pdf.
    • (2011) Machine Learning Reports , vol.5 , pp. 1-16
    • Kästner, M.1    Villmann, T.2
  • 8
    • 0027632248 scopus 로고
    • 'Neural-gas' network for vector quantization and its application to time-series prediction
    • T. M. Martinetz, S. G. Berkovich, and K. J. Schulten. 'Neural-gas' network for vector quantization and its application to time-series prediction. IEEE Trans. on Neural Networks, 4(4):558-569, 1993.
    • (1993) IEEE Trans. On Neural Networks , vol.4 , Issue.4 , pp. 558-569
    • Martinetz, T.M.1    Berkovich, S.G.2    Schulten, K.J.3
  • 9
    • 74049159263 scopus 로고    scopus 로고
    • The Dissimilarity Representation for Pattern Recognition: Foundations and Applications
    • E. Pekalska and R. Duin. The Dissimilarity Representation for Pattern Recognition: Foundations and Applications. World Scientific, 2006.
    • (2006) World Scientific
    • Pekalska, E.1    Duin, R.2
  • 11
    • 70449713460 scopus 로고    scopus 로고
    • Distance learning in discriminative vector quantization
    • P. Schneider, B. Hammer, and M. Biehl. Distance learning in discriminative vector quantization. Neural Computation, 21:2942-2969, 2009.
    • (2009) Neural Computation , vol.21 , pp. 2942-2969
    • Schneider, P.1    Hammer, B.2    Biehl, M.3
  • 12
    • 79958244935 scopus 로고    scopus 로고
    • Divergence based vector quantization
    • T. Villmann and S. Haase. Divergence based vector quantization. Neural Computation, 23(5):1343-1392, 2011.
    • (2011) Neural Computation , vol.23 , Issue.5 , pp. 1343-1392
    • Villmann, T.1    Haase, S.2
  • 13
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    • Fuzzy supervised neural gas with sparsity constraint
    • MLR-05-2011, ISSN:1865-3960
    • T. Villmann and M. Kästner. Fuzzy supervised neural gas with sparsity constraint. Machine Learning Reports, 5(MLR-05-2011):17-20, 2011. ISSN:1865-3960, http://www.techfak.uni-bielefeld.de/~fschleif/mlr/mlr_05_2011.pdf.
    • (2011) Machine Learning Reports , vol.5 , pp. 17-20
    • Villmann, T.1    Kästner, M.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.